A machine translator can be used to automatically translate a phrase from one source language to another target language. One mechanism for doing so uses a phrase table that specifies mappings from source phrases in one language to target phrases in another language. There may be several ways to translate a given word and the phrase table can include each of these mappings. Each source-target pair can be associated in the phrase table with one or more attributes such as the probability with which that particular mapping has been used, the number of times that mapping has been used, etc. An example of a phrase table entry is shown in Table 1.
TABLE 1SourceTargetCountProbabilitycapital WashingtonHauptstadt Washington270.6
In the above table, the English source phrase “capital Washington” is translated to German target phrase “Hauptstadt Washington.” The count value signifies that 27 such translations were found in the training data, and that the training procedure assigned a probability of 0.6 to this translation; a probability of 0.4 is assigned to translations into other phrases (not shown).
Table 2 shows a phrase table where the same English source phrase may be translated into several different German target phrases, along with their associated counts and probabilities.
TABLE 2SourceTarget(English)(German)CountProbabilityHelloHallo80000.4 HelloGuten Tag 3000.1 HelloServus 170.02
Automatic machine translation training procedures can cause translators to generate incorrect or less desirable translation alternatives. Table 3 shows a phrase table with two entries, one of which is correct and the other of which is wrong. The first entry properly translates the English “capital Washington” to the German “Hauptstadt Washington.” The second entry incorrectly translates the same English phrase to the German “Hauptstadt Moskau.”
TABLE 3SourceTarget(English)(German)CountProbabilitycapitalHauptstadt270.6WashingtonWashingtoncapitalHauptstadt 30.1WashingtonMoskau
It is desirable to improve automatic machine translation.